Many CSL methods are trained on large unlabelled datasets using an assortment of pretext tasks. These tedious training processes are time consuming, sometimes requiring several days of compute time [1]}. This could have a net negative impact due to the increase in their energy consumption compared to supervised learning, which might converge to useful solutions faster [2]}, [3]}.
